一、安装配置环境

代码地址 GitHub – botaoye/OSTrack: [ECCV 2022] Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework

按照官网的 option1 方法，在根目录下执行

conda create -n ostrack python=3.8
conda activate ostrack
bash install.sh

二、运行测试，遇到的问题

1、按照官网，首先建立各种路径，

执行

python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir ./output

遇到问题

ImportError: libGL.so.1: cannot open shared object file: No such file or directory

解决办法

apt-get install libgl1

运行上述脚本后，会在 /root/data/zjx/Code-subject/OSTrack-main/lib/train/admin 目录下生成 local.py 文件以及 lib/test/evaluation/ 下的 local.py 。里面是各种路径的默认设置。

2、建立训练数据集

在项目根目录路径下按照官网的格式设立。然

python tracking/train.py --script ostrack --config vitb_256_mae_ce_32x4_ep300 --save_dir ./output --mode single --use_wandb 1

后设立预训练权重文件，创建 pretrained_models 文件夹。

3、运行train 测试

在终端运行按官网来。在本地编译器需要运行的是 lib/train/run_train.py ，其中的参数设置成

Namespace(config=’vitb_256_mae_ce_32x4_ep300′, config_prv=’baseline’, config_teacher=None, distill=0, ip=’127.0.0.1′, mode=’single’, nproc_per_node=None, port=20000, rank=None, save_dir=’./output’, script=’ostrack’, script_prv=None, script_teacher=None, use_lmdb=0, use_wandb=0, world_size=None)

--script ostrack

--config vitb_256_mae_ce_32x4_ep300

--save_dir ./output

--use_lmdb 0

--script_prv None

--config_prv baseline

--distill 0

--script_teacher None

--config_teacher None

--use_wandb 0

当时只用了GOT10k 一个数据集做运行测试，所以需要去相应的配置文件下注销掉其它用到的数据集。

--script ostrack --config vitb_256_mae_ce_32x4_ep300

去这个文件下更改

  TRAIN:
    DATASETS_NAME:
#    - LASOT
    - GOT10K_vottrain
#    - COCO17
#    - TRACKINGNET

终端运行时单卡训练时需要设置参数 –mode single。那个 wandb 先不用设置，实现需要创建账户的

python tracking/train.py --script ostrack --config vitb_256_mae_ce_32x4_ep300 --save_dir ./output --mode single

遇到的问题

1）

Traceback (most recent call last):
  File "/root/data/zjx/Code-subject/OSTrack-main/lib/train/../../lib/train/trainers/base_trainer.py", line 85, in train
    self.train_epoch()
  File "/root/data/zjx/Code-subject/OSTrack-main/lib/train/../../lib/train/trainers/ltr_trainer.py", line 133, in train_epoch
    self.cycle_dataset(loader)
  File "/root/data/zjx/Code-subject/OSTrack-main/lib/train/../../lib/train/trainers/ltr_trainer.py", line 74, in cycle_dataset
    for i, data in enumerate(loader, 1):
  File "/root/anaconda3/envs/ostrack/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
    data = self._next_data()
  File "/root/anaconda3/envs/ostrack/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
    return self._process_data(data)
  File "/root/anaconda3/envs/ostrack/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
    data.reraise()
  File "/root/anaconda3/envs/ostrack/lib/python3.8/site-packages/torch/_utils.py", line 425, in reraise
    raise self.exc_type(msg)
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/root/anaconda3/envs/ostrack/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
    data = fetcher.fetch(index)
  File "/root/anaconda3/envs/ostrack/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/root/anaconda3/envs/ostrack/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/root/data/zjx/Code-subject/OSTrack-main/lib/train/../../lib/train/data/sampler.py", line 98, in __getitem__
    return self.getitem()
  File "/root/data/zjx/Code-subject/OSTrack-main/lib/train/../../lib/train/data/sampler.py", line 108, in getitem
    dataset = random.choices(self.datasets, self.p_datasets)[0]
  File "/root/anaconda3/envs/ostrack/lib/python3.8/random.py", line 404, in choices
    raise ValueError('The number of weights does not match the population')
ValueError: The number of weights does not match the population

解决办法：

第一个问题去yaml设置文件中将 num_worker 设置为0


  NUM_WORKER: 0

第二个 debug 截图所示

根据问题出处 lib\train\data\sampler.py — 109

  dataset = random.choices(self.datasets, self.p_datasets)[0]

替换（因为测试运行时只用了一个数据集 GOT10k）

dataset = self.datasets[0]

继续运行测试，遇到

FileNotFoundError: [Errno 2] No such file or directory: '/root/data/zjx/Code-subject/OSTrack-main/tracking/data/got10k/train/GOT-10k_Train_008341/groundtruth.txt'

解决办法：GOT10k数据集的格式改一下，将 train 文件夹下的所有 split 文件夹下的文件放到 train下即可。

继续运行测试，遇到

RuntimeError: CUDA out of memory. Tried to allocate 24.00 MiB (GPU 0; 10.76 GiB total capacity; 9.68 GiB already allocated; 13.56 MiB free; 9.74 GiB reserved in total by PyTorch)

解决办法，去 yaml 文件调小 batch size。

三、阅读代码记录

网络结构

OSTrack(
  (backbone): VisionTransformerCE(
    (patch_embed): PatchEmbed(
      (proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
      (norm): Identity()
    )
    (pos_drop): Dropout(p=0.0, inplace=False)
    (blocks): Sequential(
      (0): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): Identity()
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
      (1): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): DropPath(drop_prob=0.009)
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
      (2): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): DropPath(drop_prob=0.018)
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
      (3): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): DropPath(drop_prob=0.027)
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
      (4): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): DropPath(drop_prob=0.036)
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
      (5): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): DropPath(drop_prob=0.045)
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
      (6): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): DropPath(drop_prob=0.055)
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
      (7): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): DropPath(drop_prob=0.064)
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
      (8): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): DropPath(drop_prob=0.073)
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
      (9): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): DropPath(drop_prob=0.082)
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
      (10): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): DropPath(drop_prob=0.091)
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
      (11): CEBlock(
        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (attn): Attention(
          (qkv): Linear(in_features=768, out_features=2304, bias=True)
          (attn_drop): Dropout(p=0.0, inplace=False)
          (proj): Linear(in_features=768, out_features=768, bias=True)
          (proj_drop): Dropout(p=0.0, inplace=False)
        )
        (drop_path): DropPath(drop_prob=0.100)
        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
        (mlp): Mlp(
          (fc1): Linear(in_features=768, out_features=3072, bias=True)
          (act): GELU()
          (drop1): Dropout(p=0.0, inplace=False)
          (fc2): Linear(in_features=3072, out_features=768, bias=True)
          (drop2): Dropout(p=0.0, inplace=False)
        )
      )
    )
    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
  )
  (box_head): CenterPredictor(
    (conv1_ctr): Sequential(
      (0): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv2_ctr): Sequential(
      (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv3_ctr): Sequential(
      (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv4_ctr): Sequential(
      (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv5_ctr): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1))
    (conv1_offset): Sequential(
      (0): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv2_offset): Sequential(
      (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv3_offset): Sequential(
      (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv4_offset): Sequential(
      (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv5_offset): Conv2d(32, 2, kernel_size=(1, 1), stride=(1, 1))
    (conv1_size): Sequential(
      (0): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv2_size): Sequential(
      (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv3_size): Sequential(
      (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv4_size): Sequential(
      (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv5_size): Conv2d(32, 2, kernel_size=(1, 1), stride=(1, 1))
  )
)

1、打印日志处

script_name: ostrack.py  config_name: vitb_256_mae_ce_32x4_ep300.yaml

run_training.py — 42

New configuration is shown below.
MODEL configuration: {'PRETRAIN_FILE': 'mae_pretrain_vit_base.pth', 'EXTRA_MERGER': False, 'RETURN_INTER': False, 'RETURN_STAGES': [], 'BACKBONE': {'TYPE': 'vit_base_patch16_224_ce', 'STRIDE': 16, 'MID_PE': False, 'SEP_SEG': False, 'CAT_MODE': 'direct', 'MERGE_LAYER': 0, 'ADD_CLS_TOKEN': False, 'CLS_TOKEN_USE_MODE': 'ignore', 'CE_LOC': [3, 6, 9], 'CE_KEEP_RATIO': [0.7, 0.7, 0.7], 'CE_TEMPLATE_RANGE': 'CTR_POINT'}, 'HEAD': {'TYPE': 'CENTER', 'NUM_CHANNELS': 256}}
TRAIN configuration: {'LR': 0.0004, 'WEIGHT_DECAY': 0.0001, 'EPOCH': 300, 'LR_DROP_EPOCH': 240, 'BATCH_SIZE': 4, 'NUM_WORKER': 0, 'OPTIMIZER': 'ADAMW', 'BACKBONE_MULTIPLIER': 0.1, 'GIOU_WEIGHT': 2.0, 'L1_WEIGHT': 5.0, 'FREEZE_LAYERS': [0], 'PRINT_INTERVAL': 50, 'VAL_EPOCH_INTERVAL': 20, 'GRAD_CLIP_NORM': 0.1, 'AMP': False, 'CE_START_EPOCH': 20, 'CE_WARM_EPOCH': 80, 'DROP_PATH_RATE': 0.1, 'SCHEDULER': {'TYPE': 'step', 'DECAY_RATE': 0.1}}
DATA configuration: {'SAMPLER_MODE': 'causal', 'MEAN': [0.485, 0.456, 0.406], 'STD': [0.229, 0.224, 0.225], 'MAX_SAMPLE_INTERVAL': 200, 'TRAIN': {'DATASETS_NAME': ['GOT10K_vottrain'], 'DATASETS_RATIO': [1, 1, 1, 1], 'SAMPLE_PER_EPOCH': 60000}, 'VAL': {'DATASETS_NAME': ['GOT10K_votval'], 'DATASETS_RATIO': [1], 'SAMPLE_PER_EPOCH': 10000}, 'SEARCH': {'SIZE': 256, 'FACTOR': 4.0, 'CENTER_JITTER': 3, 'SCALE_JITTER': 0.25, 'NUMBER': 1}, 'TEMPLATE': {'NUMBER': 1, 'SIZE': 128, 'FACTOR': 2.0, 'CENTER_JITTER': 0, 'SCALE_JITTER': 0}}

train_script.py — 32 33

3）

No matching checkpoint file found

base_trainer.py — 174

[train: 1, 50 / 15000] FPS: 5.9 (5.0)  ,  DataTime: 0.508 (0.002)  ,  ForwardTime: 0.171  ,  TotalTime: 0.681  ,  Loss/total: 50.35498  ,  Loss/giou: 1.22484  ,  Loss/l1: 0.28600  ,  Loss/location: 46.47531  ,  IoU: 0.07033

ltr_trainer.py — 112

2、debug 参数记录

1、settings

2、config

{'MODEL': {'PRETRAIN_FILE': 'mae_pretrain_vit_base.pth', 'EXTRA_MERGER': False, 'RETURN_INTER': False, 'RETURN_STAGES': [], 'BACKBONE': {'TYPE': 'vit_base_patch16_224', 'STRIDE': 16, 'MID_PE': False, 'SEP_SEG': False, 'CAT_MODE': 'direct', 'MERGE_LAYER': 0, 'ADD_CLS_TOKEN': False, 'CLS_TOKEN_USE_MODE': 'ignore', 'CE_LOC': [], 'CE_KEEP_RATIO': [], 'CE_TEMPLATE_RANGE': 'ALL'}, 'HEAD': {'TYPE': 'CENTER', 'NUM_CHANNELS': 256}}, 'TRAIN': {'LR': 0.0001, 'WEIGHT_DECAY': 0.0001, 'EPOCH': 500, 'LR_DROP_EPOCH': 400, 'BATCH_SIZE': 16, 'NUM_WORKER': 8, 'OPTIMIZER': 'ADAMW', 'BACKBONE_MULTIPLIER': 0.1, 'GIOU_WEIGHT': 2.0, 'L1_WEIGHT': 5.0, 'FREEZE_LAYERS': [0], 'PRINT_INTERVAL': 50, 'VAL_EPOCH_INTERVAL': 20, 'GRAD_CLIP_NORM': 0.1, 'AMP': False, 'CE_START_EPOCH': 20, 'CE_WARM_EPOCH': 80, 'DROP_PATH_RATE': 0.1, 'SCHEDULER': {'TYPE': 'step', 'DECAY_RATE': 0.1}}, 'DATA': {'SAMPLER_MODE': 'causal', 'MEAN': [0.485, 0.456, 0.406], 'STD': [0.229, 0.224, 0.225], 'MAX_SAMPLE_INTERVAL': 200, 'TRAIN': {'DATASETS_NAME': ['LASOT', 'GOT10K_vottrain'], 'DATASETS_RATIO': [1, 1], 'SAMPLE_PER_EPOCH': 60000}, 'VAL': {'DATASETS_NAME': ['GOT10K_votval'], 'DATASETS_RATIO': [1], 'SAMPLE_PER_EPOCH': 10000}, 'SEARCH': {'SIZE': 320, 'FACTOR': 5.0, 'CENTER_JITTER': 4.5, 'SCALE_JITTER': 0.5, 'NUMBER': 1}, 'TEMPLATE': {'NUMBER': 1, 'SIZE': 128, 'FACTOR': 2.0, 'CENTER_JITTER': 0, 'SCALE_JITTER': 0}}, 'TEST': {'TEMPLATE_FACTOR': 2.0, 'TEMPLATE_SIZE': 128, 'SEARCH_FACTOR': 5.0, 'SEARCH_SIZE': 320, 'EPOCH': 500}}

3、actor

4、self.loaders

5、最终的 out

6、gt_dict

7、pred_dict

8、model_kwargs

9、data

10、index

11、checkpoint

12、dir_list

dir_list

13、seq_ids

14、self.sequence_list

15、meta_info

[‘[METAINFO]\n’, ‘url: https://youtu.be/ZyPZRpP9dDg\n’, ‘begin: 00:00:32\n’, ‘end: 00:00:41\n’, ‘anno_fps: 10Hz\n’, ‘object_class: ichneumon\n’, ‘motion_class: walking\n’, ‘major_class: viverrine\n’, ‘root_class: animal\n’, ‘motion_adverb: slowly\n’, ‘resolution: (1920, 1080)’]

16、object_meta

17、

18、

19、

排序后

20、

21、

22、

23、

24、

25、

26、

27、

3、一些入口

1） dataloader 的建立

train_script.py — 48

loader_train, loader_val = build_dataloaders(cfg, settings)

2) 创建模型

train_script.py — 55

net = build_ostrack(cfg)

这里面包括预训练权重的加载，以及加载整个模型

3） Loss actor 以及 optimer等

train_script.py — 71 始

这里的 actor 就是执行训练过程的

4）train 过程开始

train_script.py —88

5) 数据送入模型

在这上面的是

actors/ostrack.py — 69

这里才算是数据送入模型的开始

ostrack\ostrack.py — 40

6）forward pass

actors\ostrack.py — 31

前向传播过程

7）compute loss

actors\ostrack.py — 34

8) 断点续训

base_trainer.py — 169

4、模型处理过程

1) 送入backbone

 x, aux_dict = self.backbone(z=template, x=search,
                                    ce_template_mask=ce_template_mask,
                                    ce_keep_rate=ce_keep_rate,
                                    return_last_attn=return_last_attn, )  # 跳转到 vit_ce.py---191  x Tensor:(4,320,768)

首先进行 patch_embed

x = self.patch_embed(x)
z = self.patch_embed(z)

处理过程为

    def forward(self, x):
        # allow different input size
        # B, C, H, W = x.shape
        # _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
        # _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
        x = self.proj(x)  # Tensor:(4,768,16,16)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC  # Tensor:(4,256,768)
        x = self.norm(x)  # Tensor:(4,256,768)
        return x

先经过 16X16 的卷积，然后再拉直

文中的

attn.py — 37

qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)

得到输出的bbox过程

    def cal_bbox(self, score_map_ctr, size_map, offset_map, return_score=False):
        max_score, idx = torch.max(score_map_ctr.flatten(1), dim=1, keepdim=True)  # shape都是 Tensor:(4,1) 按 batch 拿出最大的得分和所对应的索引
        idx_y = idx // self.feat_sz  # Tensor:(4,1)
        idx_x = idx % self.feat_sz  # Tensor:(4,1)

        idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1)  # Tensor:(4,2,1)
        size = size_map.flatten(2).gather(dim=2, index=idx)  # Tensor:(4,2,1)
        offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1)  # Tensor:(4,2)

        # bbox = torch.cat([idx_x - size[:, 0] / 2, idx_y - size[:, 1] / 2,
        #                   idx_x + size[:, 0] / 2, idx_y + size[:, 1] / 2], dim=1) / self.feat_sz
        # cx, cy, w, h
        bbox = torch.cat([(idx_x.to(torch.float) + offset[:, :1]) / self.feat_sz,
                          (idx_y.to(torch.float) + offset[:, 1:]) / self.feat_sz,
                          size.squeeze(-1)], dim=1)  # Tensor:(4,4)

        if return_score:
            return bbox, max_score
        return bbox

5、加载预训练 backbone

骨干网络模型定义处

vit_ce.py — 197

backbone结构：

VisionTransformerCE(
  (patch_embed): PatchEmbed(
    (proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
    (norm): Identity()
  )
  (pos_drop): Dropout(p=0.0, inplace=False)
  (blocks): Sequential(
    (0): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): Identity()
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
    (1): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath(drop_prob=0.009)
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
    (2): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath(drop_prob=0.018)
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
    (3): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath(drop_prob=0.027)
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
    (4): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath(drop_prob=0.036)
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
    (5): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath(drop_prob=0.045)
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
    (6): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath(drop_prob=0.055)
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
    (7): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath(drop_prob=0.064)
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
    (8): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath(drop_prob=0.073)
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
    (9): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath(drop_prob=0.082)
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
    (10): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath(drop_prob=0.091)
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
    (11): CEBlock(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath(drop_prob=0.100)
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU()
        (drop1): Dropout(p=0.0, inplace=False)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop2): Dropout(p=0.0, inplace=False)
      )
    )
  )
  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
)

加载的cfg文件内容

{‘MODEL’: {‘PRETRAIN_FILE’: ‘mae_pretrain_vit_base.pth’, ‘EXTRA_MERGER’: False, ‘RETURN_INTER’: False, ‘RETURN_STAGES’: [], ‘BACKBONE’: {‘TYPE’: ‘vit_base_patch16_224_ce’, ‘STRIDE’: 16, ‘MID_PE’: False, ‘SEP_SEG’: False, ‘CAT_MODE’: ‘direct’, ‘MERGE_LAYER’: 0, ‘ADD_CLS_TOKEN’: False, ‘CLS_TOKEN_USE_MODE’: ‘ignore’, ‘CE_LOC’: [3, 6, 9], ‘CE_KEEP_RATIO’: [0.7, 0.7, 0.7], ‘CE_TEMPLATE_RANGE’: ‘CTR_POINT’}, ‘HEAD’: {‘TYPE’: ‘CENTER’, ‘NUM_CHANNELS’: 256}}, ‘TRAIN’: {‘LR’: 0.0004, ‘WEIGHT_DECAY’: 0.0001, ‘EPOCH’: 300, ‘LR_DROP_EPOCH’: 240, ‘BATCH_SIZE’: 4, ‘NUM_WORKER’: 0, ‘OPTIMIZER’: ‘ADAMW’, ‘BACKBONE_MULTIPLIER’: 0.1, ‘GIOU_WEIGHT’: 2.0, ‘L1_WEIGHT’: 5.0, ‘FREEZE_LAYERS’: [0], ‘PRINT_INTERVAL’: 50, ‘VAL_EPOCH_INTERVAL’: 20, ‘GRAD_CLIP_NORM’: 0.1, ‘AMP’: False, ‘CE_START_EPOCH’: 20, ‘CE_WARM_EPOCH’: 80, ‘DROP_PATH_RATE’: 0.1, ‘SCHEDULER’: {‘TYPE’: ‘step’, ‘DECAY_RATE’: 0.1}}, ‘DATA’: {‘SAMPLER_MODE’: ‘causal’, ‘MEAN’: [0.485, 0.456, 0.406], ‘STD’: [0.229, 0.224, 0.225], ‘MAX_SAMPLE_INTERVAL’: 200, ‘TRAIN’: {‘DATASETS_NAME’: [‘GOT10K_vottrain’], ‘DATASETS_RATIO’: [1, 1, 1, 1], ‘SAMPLE_PER_EPOCH’: 60000}, ‘VAL’: {‘DATASETS_NAME’: [‘GOT10K_votval’], ‘DATASETS_RATIO’: [1], ‘SAMPLE_PER_EPOCH’: 10000}, ‘SEARCH’: {‘SIZE’: 256, ‘FACTOR’: 4.0, ‘CENTER_JITTER’: 3, ‘SCALE_JITTER’: 0.25, ‘NUMBER’: 1}, ‘TEMPLATE’: {‘NUMBER’: 1, ‘SIZE’: 128, ‘FACTOR’: 2.0, ‘CENTER_JITTER’: 0, ‘SCALE_JITTER’: 0}}, ‘TEST’: {‘TEMPLATE_FACTOR’: 2.0, ‘TEMPLATE_SIZE’: 128, ‘SEARCH_FACTOR’: 4.0, ‘SEARCH_SIZE’: 256, ‘EPOCH’: 300}}

6、标签的设计

gt_guassuan_pans

它的设立跟 gt_bbox 的有关，这个是分类标签

7、datalodaer的创建

train_script.py — 48

用到了数据增强。

数据的加载，这个就与Dataloader 与 Dataset 的机制有关了。自己定义导入数据时需要继承 Dataset 父类，并重写 __len__ 和 __getitem__ 方法。

这里的代码实现主要在 data\sampler.py 文件中。

对于got10k，每个视频序列下包含的文件如下所示

其中 absence.label 是occlusion 的，内部内容举例

        with open(occlusion_file, 'r', newline='') as f:
            occlusion = torch.ByteTensor([int(v[0]) for v in csv.reader(f)])

# =======
tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=torch.uint8)

cover.label 举例

        with open(cover_file, 'r', newline='') as f:
            cover = torch.ByteTensor([int(v[0]) for v in csv.reader(f)])  # Tensor:(110,)

# =================
tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7, 5, 1, 2, 4, 5, 8, 8, 8, 8,
        8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
        8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
        8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
        8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], dtype=torch.uint8)

视频序列采样时，模板帧需要在 search 前面

# Sample test and train frames in a causal manner, i.e. search_frame_ids > template_frame_ids

建立一个训练单位 data

sampler.py — 157

data = TensorDict({'template_images': template_frames,
                                   'template_anno': template_anno['bbox'],
                                   'template_masks': template_masks,
                                   'search_images': search_frames,
                                   'search_anno': search_anno['bbox'],
                                   'search_masks': search_masks,
                                   'dataset': dataset.get_name(),
                                   'test_class': meta_obj_test.get('object_class_name')})

裁剪的区域是根据bbox 来的，将输入 resize成 128X128 processing_utils.py — 68

输入的normalize 过程

transforms.py — 255

输入数据的数据增强操作顺序

范围（0~255）归一化到（0，1）
然后进行数据增强
最后归一化
    def transform_image(self, image):
        return tvisf.normalize(image, self.mean, self.std, self.inplace)

随机数的影响 transforms.py — 102

rand_params = self.roll()

模板时

搜索区域时

说明随机数对于模板和搜索区域不统一。

8、数据的加载过程

数据的加载过程都是在 sampler 中实现的，它重写了 Dataset 类中的方法，所以Dataloadre 加载导入输入数据时从这里进行。

class TrackingSampler(torch.utils.data.Dataset):

而 processing 中的内容是对原始的输入数据进行处理，在这里面包括裁剪resize，数据增强，归一化等处理。

注意，是否使用 lmdb 是由 use_lmdb 参数决定的。

2、最终的预测

 def forward(self, x, gt_score_map=None):
        """ Forward pass with input x. """
        score_map_ctr, size_map, offset_map = self.get_score_map(x)  # Tensor:(4,1,16,16) , Tensor:(4,2,16,16), Tensor:(4,2,16,16)

        # assert gt_score_map is None
        if gt_score_map is None:  # True
            bbox = self.cal_bbox(score_map_ctr, size_map, offset_map)  # Tensor:(4,4)
        else:
            bbox = self.cal_bbox(gt_score_map.unsqueeze(1), size_map, offset_map)

        return score_map_ctr, bbox, size_map, offset_map

都用上了，中和这些计算bbox head.py — 131

3、保存训练的模型

base_trainer.py –198

 # only save the last 10 checkpoints
                    save_every_epoch = getattr(self.settings, "save_every_epoch", False)
                    save_epochs = [79, 159, 239]
                    if epoch > (max_epochs - 1) or save_every_epoch or epoch % 40 == 0 or epoch in save_epochs or epoch > (max_epochs - 5):
                    # if epoch > (max_epochs - 10) or save_every_epoch or epoch % 100 == 0:
                        if self._checkpoint_dir:
                            if self.settings.local_rank in [-1, 0]:
                                self.save_checkpoint()

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